Modeling cognitive behavior of human errors based on ACT-R: Design of color cued operation switching task

Abstract: Although the mechanization of labor and the automation of work have been advancing in recent years, accidents caused by human error continue to occur. To prevent such accidents, it is essential to identify human errors and understand their underlying mechanisms. One common approach in research is to conduct experiments using cognitive tasks to measure human error and analyze its mechanisms based on the obtained data. However, few studies have attempted to understand the data generation process by simulating the rules governing measurement data using cognitive models. Therefore, this study aims to construct a cognitive model using the ACT-R cognitive architecture and to develop a cognitive task for measuring cognitive behavior and human error. Furthermore, by conducting experiments with the developed cognitive task, we will examine whether it is possible to measure cognitive behavior when human error occurs. Specifically, we designed a cognitive task in which participants perceive two displays, each showing two numbers in a predetermined order, perform arithmetic operations, and input their answers using a keyboard. The task incorporates four colors for the displayed numbers (green, purple, red, and black), with each color corresponding to a different arithmetic operation. To achieve precise measurement, a video camera was placed between the two displays to capture the participants’ faces from the front, enabling accurate eye-gaze tracking. A total of six undergraduate and graduate students aged 18 to 29, all enrolled at Kyoto University, participated in the experiment. The experiment was conducted individually, and each participant completed the cognitive task seven times, including practice trials. By comparing the results with those of previous studies, we confirmed that the error rate was significantly improved.

Keywords: Human Error, Human Performance, Cognitive Behavior, Cognitive Task, Eye Movement, Cognitive Architecture

DOI: 10.54941/ahfe1006278

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